In this paper, we propose a new infrared and visible image fusion method based on multi-
scale transformation and norm optimization. In this method, a new loss function is designed …

Image reconstruction from undersampled k-space data has been playing an important role
in fast magnetic resonance imaging (MRI). Recently, deep learning has demonstrated …

Compressed sensing (CS) has exhibited great potential for accelerating magnetic
resonance imaging (MRI). In CS-MRI, we want to reconstruct a high-quality image from very …

We develop a novel edge-preserving filtering retinex algorithm for single underwater image
enhancement, in which gradient domain guided image filtering (GGF) priors of reflection and …

Image reconstruction from undersampled k-space data has been playing an important role
for fast MRI. Recently, deep learning has demonstrated tremendous success in various …

To improve the compressive sensing MRI (CS-MRI) approaches in terms of fine structure
loss under high acceleration factors, we have proposed an iterative feature refinement …

One of the major concerns in photoacoustic computed tomography (PACT) is obtaining a
high-quality image using the minimum number of ultrasound transducers/view angles. This …

Deep learning, particularly the generative model, has demonstrated tremendous potential to
significantly speed up image reconstruction with reduced measurements recently. Rather …

Deep learning has proven itself to be able to reduce the scanning time of Magnetic
Resonance Imaging (MRI) and to improve the image reconstruction quality since it was …

Compressed sensing (CS) can speed up the magnetic resonance imaging (MRI) process
and reconstruct high-quality images from under-sampled k-space data. However, traditional …